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Found 69 Skills
Prometheus monitoring and alerting for cloud-native observability. USE WHEN: Writing PromQL queries, configuring Prometheus scrape targets, creating alerting rules, setting up recording rules, instrumenting applications with Prometheus metrics, configuring service discovery. DO NOT USE: For building dashboards (use /grafana), for log analysis (use /logging-observability), for general observability architecture (use senior-software-engineer with infrastructure focus). TRIGGERS: metrics, prometheus, promql, counter, gauge, histogram, summary, alert, alertmanager, alerting rule, recording rule, scrape, target, label, service discovery, relabeling, exporter, instrumentation, slo, error budget.
Instrument applications with OpenTelemetry SDK and validate telemetry using Kopai. Use when setting up observability, adding tracing/logging/metrics, testing instrumentation, or debugging missing telemetry data.
Use when an existing agent needs Prefactor resources created via the Prefactor CLI before SDK instrumentation is added.
Use when choosing which Prefactor SDK skill to load for agent instrumentation or for building a custom provider integration on top of @prefactor/core.
Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source of invalid data or incorrect behavior
Use this skill when working with SigNoz - open-source observability platform for application monitoring, distributed tracing, log management, metrics, alerts, and dashboards. Triggers on SigNoz setup, OpenTelemetry instrumentation for SigNoz, sending traces/logs/metrics to SigNoz, creating SigNoz dashboards, configuring SigNoz alerts, exception monitoring, and migrating from Datadog/Grafana/New Relic to SigNoz.
Specifies event tracking and analytics instrumentation requirements for a feature. Use when defining what data to collect, ensuring consistent tracking implementation, or documenting analytics requirements for engineering.
Code instrumentation for timing workloads. Two scenarios: (1) Training loop — inject manual timing to report per-iteration latency, throughput (samples/sec), and data load time. (2) Standalone kernel/op — write CUDA event timing code with warmup, per-iteration statistics, and anti-pattern avoidance. Also covers NVTX annotation for labeling profiler timelines. NOT for: running or analyzing profiler tools (nsys, ncu, Nsight Systems, Nsight Compute), writing kernels (Triton, CuTe, CUDA), applying optimizations (CUDA Graphs, gradient checkpointing, fusion), or interpreting roofline/SOL% metrics. Triggers: "measure throughput", "benchmark this function", "time my training loop", "samples per second", "NVTX annotate", "instrument my dataloader", "data load time", "kernel timing", "how do I time".
Enforce safe Frida 17 MCP operations with strict phase gating. Use when attaching/spawning sessions, writing or loading scripts, applying Java/native hooks, troubleshooting instrumentation failures, or interpreting Frida MCP output. Require docs-first usage and run the mandatory workflow: Idea -> Scripting -> Execution -> Notes.